Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA319199 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Analysis of somatic DNA copy number alterations and frequency of breast cancer intrinsic subtypes from Mexican women [copy number]
| PRJNA343312 | ENA
Project description:Germline copy number variations and breast cancer risk
| PRJNA674608 | ENA
Project description:In the United States, African-American (AA) women are more likely to develop early-onset breast cancer and have historically poorer outcomes due to this disease compared to European-American (EA) women. Here, we analyzed genomic profiles of breast tumors from young women (<50 years old), matched by tumor subtype, histological grade, and ethnicity (African-American, AA, compared to European-American, EA). DNA copy number alterations (CNAs) were analyzed on the Affymetrix Human SNP Array v 6.0 platform. The study provides insight into the genetic component of ethnicity-related breast cancer health disparities.
2013-01-29 | GSE26232 | GEO
Project description:In the United States, African-American (AA) women are more likely to develop early-onset breast cancer and have historically poorer outcomes due to this disease compared to European-American (EA) women. Here, we analyzed genomic profiles of breast tumors from young women (<50 years old), matched by tumor subtype, histological grade, and ethnicity (African-American, AA, compared to European-American, EA). DNA copy number alterations (CNAs) were analyzed using a 32K BAC tiling path array. The study provides insight into the genetic component of ethnicity-related breast cancer health disparities.
2013-01-29 | GSE26214 | GEO
Project description:The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers. We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival. Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer. We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.
2014-01-07 | E-GEOD-48297 | biostudies-arrayexpress
Project description:The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers. We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival. Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer. We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.
2014-01-07 | E-GEOD-48390 | biostudies-arrayexpress
Project description:FFPE study using MIP copy number platform - breast cancer I
| PRJNA114333 | ENA
Project description:FFPE study using MIP copy number platform - breast cancer II
| PRJNA114335 | ENA